{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Train and explain models remotely via Azure Machine Learning Compute\n",
        "\n",
        "\n",
        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to train and explain a regression model remotely on an Azure Machine Learning Compute Target (AMLCompute).**_\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "## Table of Contents\n",
        "\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "    1. Initialize a Workspace\n",
        "    1. Create an Experiment\n",
        "    1. Introduction to AmlCompute\n",
        "    1. Submit an AmlCompute run\n",
        "1. Additional operations to perform on AmlCompute\n",
        "1. [Download model explanations from Azure Machine Learning Run History](#Download)\n",
        "1. [Visualize explanations](#Visualize)\n",
        "1. [Next steps](#Next)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "\n",
        "This notebook showcases how to train and explain a regression model remotely via Azure Machine Learning Compute (AMLCompute), and download the calculated explanations locally for visualization.\n",
        "It demonstrates the API calls that you need to make to submit a run for training and explaining a model to AMLCompute, download the compute explanations remotely, and visualizing the global and local explanations via a visualization dashboard that provides an interactive way of discovering patterns in model predictions and downloaded explanations.\n",
        "\n",
        "We will showcase one of the tabular data explainers: TabularExplainer (SHAP).\n",
        "\n",
        "Problem: Boston Housing Price Prediction with scikit-learn (train a model and run an explainer remotely via AMLCompute, and download and visualize the remotely-calculated explanations.)\n",
        "\n",
        "| ![explanations-run-history](./img/explanations-run-history.png) |\n",
        "|:--:|\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n",
        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't.\n",
        "\n",
        "\n",
        "If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
        "If you are using Jupyter Labs run the following command:\n",
        "```\n",
        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
        "```\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Check core SDK version number\n",
        "import azureml.core\n",
        "\n",
        "print(\"SDK version:\", azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Initialize a Workspace\n",
        "\n",
        "Initialize a workspace object from persisted configuration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "create workspace"
        ]
      },
      "outputs": [],
      "source": [
        "from azureml.core import Workspace\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create An Experiment\n",
        "\n",
        "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Experiment\n",
        "experiment_name = 'explainer-remote-run-on-amlcompute'\n",
        "experiment = Experiment(workspace=ws, name=experiment_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction to AmlCompute\n",
        "\n",
        "Azure Machine Learning Compute is managed compute infrastructure that allows the user to easily create single to multi-node compute of the appropriate VM Family. It is created **within your workspace region** and is a resource that can be used by other users in your workspace. It autoscales by default to the max_nodes, when a job is submitted, and executes in a containerized environment packaging the dependencies as specified by the user. \n",
        "\n",
        "Since it is managed compute, job scheduling and cluster management are handled internally by Azure Machine Learning service. \n",
        "\n",
        "For more information on Azure Machine Learning Compute, please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)\n",
        "\n",
        "If you are an existing BatchAI customer who is migrating to Azure Machine Learning, please read [this article](https://aka.ms/batchai-retirement)\n",
        "\n",
        "**Note**: As with other Azure services, there are limits on certain resources (for eg. AmlCompute quota) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n",
        "\n",
        "\n",
        "The training script `train_explain.py` is already created for you. Let's have a look."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Submit an AmlCompute run\n",
        "\n",
        "First lets check which VM families are available in your region. Azure is a regional service and some specialized SKUs (especially GPUs) are only available in certain regions. Since AmlCompute is created in the region of your workspace, we will use the supported_vms () function to see if the VM family we want to use ('STANDARD_D2_V2') is supported.\n",
        "\n",
        "You can also pass a different region to check availability and then re-create your workspace in that region through the [configuration notebook](../../../configuration.ipynb)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
        "\n",
        "AmlCompute.supported_vmsizes(workspace=ws)\n",
        "# AmlCompute.supported_vmsizes(workspace=ws, location='southcentralus')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create project directory\n",
        "\n",
        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "import shutil\n",
        "\n",
        "project_folder = './explainer-remote-run-on-amlcompute'\n",
        "os.makedirs(project_folder, exist_ok=True)\n",
        "shutil.copy('train_explain.py', project_folder)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Provision a compute target\n",
        "\n",
        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
        "\n",
        "You can provision an AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
        "\n",
        "* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n",
        "* `max_nodes`: Maximum nodes to autoscale to while running a job on AmlCompute"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "# Choose a name for your CPU cluster\n",
        "cpu_cluster_name = \"cpu-cluster\"\n",
        "\n",
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
        "    print('Found existing cluster, use it.')\n",
        "except ComputeTargetException:\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
        "                                                           max_nodes=4)\n",
        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
        "\n",
        "cpu_cluster.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Configure & Run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "import sys\n",
        "\n",
        "# Create a new RunConfig object\n",
        "run_config = RunConfiguration(framework=\"python\")\n",
        "\n",
        "# Set compute target to AmlCompute target created in previous step\n",
        "run_config.target = cpu_cluster.name\n",
        "\n",
        "azureml_pip_packages = [\n",
        "    'azureml-defaults', 'azureml-telemetry', 'azureml-interpret'\n",
        "]\n",
        "\n",
        "python_version = '{0}.{1}'.format(sys.version_info[0], sys.version_info[1])\n",
        "\n",
        "# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n",
        "# In production scenario user would choose their dependencies\n",
        "import pkg_resources\n",
        "available_packages = pkg_resources.working_set\n",
        "sklearn_ver = None\n",
        "pandas_ver = None\n",
        "joblib_ver = None\n",
        "scipy_ver = None\n",
        "for dist in list(available_packages):\n",
        "    if dist.key == 'scikit-learn':\n",
        "        sklearn_ver = dist.version\n",
        "    elif dist.key == 'pandas':\n",
        "        pandas_ver = dist.version\n",
        "    elif dist.key == 'joblib':\n",
        "        joblib_ver = dist.version\n",
        "    elif dist.key == 'scipy':\n",
        "        scipy_ver = dist.version\n",
        "sklearn_dep = 'scikit-learn'\n",
        "pandas_dep = 'pandas'\n",
        "joblib_dep = 'joblib'\n",
        "scipy_dep = 'scipy'\n",
        "if sklearn_ver:\n",
        "    sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n",
        "if pandas_ver:\n",
        "    pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
        "if joblib_ver:\n",
        "    joblib_dep = 'joblib=={}'.format(joblib_ver)\n",
        "if scipy_ver:\n",
        "    scipy_dep = 'scipy=={}'.format(scipy_ver)\n",
        "# Specify CondaDependencies obj\n",
        "# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n",
        "# the submitted job is run in.  Note the remote environment(s) needs to be similar to the local\n",
        "# environment, otherwise if a model is trained or deployed in a different environment this can\n",
        "# cause errors.  Please take extra care when specifying your dependencies in a production environment.\n",
        "azureml_pip_packages.extend([sklearn_dep, pandas_dep, joblib_dep, scipy_dep])\n",
        "run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages,  python_version=python_version)\n",
        "\n",
        "from azureml.core import ScriptRunConfig\n",
        "\n",
        "src = ScriptRunConfig(source_directory=project_folder, \n",
        "                      script='train_explain.py', \n",
        "                      run_config=run_config) \n",
        "run = experiment.submit(config=src)\n",
        "run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%time\n",
        "# Shows output of the run on stdout.\n",
        "run.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run.get_metrics()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Download \n",
        "1. Download model explanation data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.interpret import ExplanationClient\n",
        "\n",
        "# Get model explanation data\n",
        "client = ExplanationClient.from_run(run)\n",
        "global_explanation = client.download_model_explanation()\n",
        "local_importance_values = global_explanation.local_importance_values\n",
        "expected_values = global_explanation.expected_values\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Or you can use the saved run.id to retrive the feature importance values\n",
        "client = ExplanationClient.from_run_id(ws, experiment_name, run.id)\n",
        "global_explanation = client.download_model_explanation()\n",
        "local_importance_values = global_explanation.local_importance_values\n",
        "expected_values = global_explanation.expected_values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Get the top k (e.g., 4) most important features with their importance values\n",
        "global_explanation_topk = client.download_model_explanation(top_k=4)\n",
        "global_importance_values = global_explanation_topk.get_ranked_global_values()\n",
        "global_importance_names = global_explanation_topk.get_ranked_global_names()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print('global importance values: {}'.format(global_importance_values))\n",
        "print('global importance names: {}'.format(global_importance_names))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "2. Download model file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Retrieve model for visualization and deployment\n",
        "from azureml.core.model import Model\n",
        "import joblib\n",
        "original_model = Model(ws, 'model_explain_model_on_amlcomp')\n",
        "model_path = original_model.download(exist_ok=True)\n",
        "original_model = joblib.load(model_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "3. Download test dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Retrieve x_test for visualization\n",
        "x_test_path = './x_test_boston_housing.pkl'\n",
        "run.download_file('x_test_boston_housing.pkl', output_file_path=x_test_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "x_test = joblib.load('x_test_boston_housing.pkl')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Visualize\n",
        "Load the visualization dashboard"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from raiwidgets import ExplanationDashboard"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ExplanationDashboard(global_explanation, original_model, dataset=x_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Next\n",
        "Learn about other use cases of the explain package on a:\n",
        "1. [Training time: regression problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-regression-local.ipynb)       \n",
        "1. [Training time: binary classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-binary-classification-local.ipynb)\n",
        "1. [Training time: multiclass classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-multiclass-classification-local.ipynb)\n",
        "1. Explain models with engineered features:\n",
        "    1. [Simple feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/simple-feature-transformations-explain-local.ipynb)\n",
        "    1. [Advanced feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/advanced-feature-transformations-explain-local.ipynb)\n",
        "1. [Save model explanations via Azure Machine Learning Run History](../run-history/save-retrieve-explanations-run-history.ipynb)\n",
        "1. Inferencing time: deploy a classification model and explainer:\n",
        "    1. [Deploy a locally-trained model and explainer](../scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
        "    1. [Deploy a locally-trained keras model and explainer](../scoring-time/train-explain-model-keras-locally-and-deploy.ipynb)\n",
        "    1. [Deploy a remotely-trained model and explainer](../scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "mesameki"
      }
    ],
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